A single-center, retrospective study of hospitalized patients with lower respiratory tract infections: clinical assessment of metagenomic next-generation sequencing and identification of risk factors in patients

Abstract

Introduction: Lower respiratory tract infections(LRTIs) in adults are complicated by diverse pathogens that challenge traditional detection methods, which are often slow and insensitive. Metagenomic next-generation sequencing (mNGS) offers a comprehensive, high-throughput, and unbiased approach to pathogen identification. This retrospective study evaluates the diagnostic efficacy of mNGS compared to conventional microbiological testing (CMT) in LRTIs, aiming to enhance detection accuracy and enable early clinical prediction.

Methods: In our retrospective single-center analysis, 451 patients with suspected LRTIs underwent mNGS testing from July 2020 to July 2023. We assessed the pathogen spectrum and compared the diagnostic efficacy of mNGS to CMT, with clinical comprehensive diagnosis serving as the reference standard. The study analyzed mNGS performance in lung tissue biopsies and bronchoalveolar lavage fluid (BALF) from cases suspected of lung infection. Patients were stratified into two groups based on clinical outcomes (improvement or mortality), and we compared clinical data and conventional laboratory indices between groups. A predictive model and nomogram for the prognosis of LRTIs were constructed using univariate followed by multivariate logistic regression, with model predictive accuracy evaluated by the area under the ROC curve (AUC).

Results: (1) Comparative Analysis of mNGS versus CMT: In a comprehensive analysis of 510 specimens, where 59 cases were concurrently collected from lung tissue biopsies and BALF, the study highlights the diagnostic superiority of mNGS over CMT. Specifically, mNGS demonstrated significantly higher sensitivity and specificity in BALF samples (82.86% vs. 44.42% and 52.00% vs. 21.05%, respectively, p < 0.001) alongside greater positive and negative predictive values (96.71% vs. 79.55% and 15.12% vs. 5.19%, respectively, p < 0.01). Additionally, when comparing simultaneous testing of lung tissue biopsies and BALF, mNGS showed enhanced sensitivity in BALF (84.21% vs. 57.41%), whereas lung tissues offered higher specificity (80.00% vs. 50.00%). (2) Analysis of Infectious Species in Patients from This Study: The study also notes a concerning incidence of lung abscesses and identifies Epstein-Barr virus (EBV), Fusobacterium nucleatum, Mycoplasma pneumoniae, Chlamydia psittaci, and Haemophilus influenzae as the most common pathogens, with Klebsiella pneumoniae emerging as the predominant bacterial culprit. Among herpes viruses, EBV and herpes virus 7 (HHV-7) were most frequently detected, with HHV-7 more prevalent in immunocompromised individuals. (3) Risk Factors for Adverse Prognosis and a Mortality Risk Prediction Model in Patients with LRTIs: We identified key risk factors for poor prognosis in lower respiratory tract infection patients, with significant findings including delayed time to mNGS testing, low lymphocyte percentage, presence of chronic lung disease, multiple comorbidities, false-negative CMT results, and positive herpesvirus affecting patient outcomes. We also developed a nomogram model with good consistency and high accuracy (AUC of 0.825) for predicting mortality risk in these patients, offering a valuable clinical tool for assessing prognosis.

Conclusion: The study underscores mNGS as a superior tool for lower respiratory tract infection diagnosis, exhibiting higher sensitivity and specificity than traditional methods.

Keywords: Diagnostic efficacy; Lower respiratory tract infections (LRTIs); Metagenomic next-generation sequencing (mNGS); Nomogram; Predictive model.

Fig. 1

A. Contribution of mNGS testing to the diagnosis of patients with lower respiratory tract infections. A.Enrollment details and study design. B. Diagnostic performance of mNGS and CMT for pathogen detection (upper panel) and diagnostic performance of mNGS in paired lavage fluid and lung tissue samples (lower panel). C. Bar graph comparing pathogen detection between mNGS and culturing, bacterial spectrum comparison between mNGS and CMT, fungal spectrum comparison between mNGS and CMT, and comparison of viruses identified by mNGS and CMT

Fig. 2

Results of mNGS testing on BALF and synchronous lung tissue samples. (A) Heatmap of species sequencing numbers detected in lavage fluid and tissue samples (sequencing numbers were normalized). (B) Feature Sankey diagram of tissue detection. Frequency and relative abundance of herpesvirus infections

Fig. 3

Detection Frequency and Abundance of Human Herpesviruses. (A) Prevalence of the five human viruses in suspected lower respiratory tract infection patients at our center, arranged by their prevalence.(Left) Viral load is presented on the x-axis as the proportion of viral sequences detected per patient sample relative to all microorganisms in that sample, with values transformed to a logarithmic scale, the bar represents the median(Right). (B) Heatmap illustrated the detection rates of various subtypes of herpesviruses across patient populations with different underlying diseases

Fig. 4

Adverse prognostic risk factors in CAP patients. (A) Nomogram and scoring methodology for adverse prognostic factors in CAP patients. Each predictive variable’s value (the line after each variable) corresponds to a score (top row), which is then totaled to obtain the overall score, determining the corresponding predictive probability (bottom row). (B) Calibration analysis of the training set, where greater alignment with the reference line indicates more precise predictions. (C) Nomogram model-generated ROC curve for the modeling set in predicting mortality risk in CAP patients